We introduce the multi-prediction deep Boltzmann machine (MP-DBM). The MPDBM can be seen as a single probabilistic model trained to maximize a variational approximation to the generalized pseudolikelihood, or as a family of recurrent nets that share parameters and approximately solve different inference problems. Prior methods of training DBMs either do not perform well on classification tasks ...

This paper studied a class of coupling problems material assignment, part nesting, kit delivery and parallel machine scheduling. The aim this was to solve the scheduling problem metal structural processing welding assembly with eligibility constraints. A two-stage mixed-integer programming model constructed. constraints took into account type parts nesting. objectives were minimize makespan, ma...

In this paper, we propose the Fuzzy Scheduling System (FSS) to deal with the dedicated machine constraint. The constraint of having a dedicated machine for photolithography process is the new issue introduced in photolithography machinery due to natural bias. If we randomly schedule the wafer lots to arbitrary photolithography machines at the first photolithography stage, then the load of all p...

Multi-machine scheduling, that is, the assigment of jobs to machines such that certain performance demands like cost and time effectiveness are fulfilled, is a ubiquitous and complex activity in everyday life. This paper presents an approach to multi-machine scheduling that follows the multiagent learning paradigm known from the field of Distributed Artificial Intelligence. According to this ap...

Twin support vector machines are a recently proposed learning method for binary classification. They learn two hyperplanes rather than one as in conventional support vector machines and often bring performance improvements. Multiview learning is concerned about learning from multiple distinct feature sets, which aims to exploit distinct views to improve generalization performance. In this paper...

In this paper, we develop a multi-objective model to optimally control the lead time of a multi-stage assembly system, using genetic algorithms. The multi-stage assembly system is modelled as an open queueing network. It is assumed that the product order arrives according to a Poisson process. In each service station, there is either one or infinite number of servers (machines) with exponential...

In several manufacturing contexts including food industry, semiconductor manufacturing, and polymers forming, the product quality deteriorates during production by prolonged exposure to the air caused by excessive lead times. Buffers increase the system throughput while also increasing the production lead time, consequently affecting the product quality. This paper proposes a theory and methodo...